"""
    Copyright 2022 Huawei Technologies Co., Ltd

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
    Typical usage example:
"""

import sys
import torch
import torch.onnx
import torch.nn as nn
import math


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, num_group=32):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes*2, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes*2)
        self.conv2 = nn.Conv2d(planes*2, planes*2, kernel_size=3, stride=stride,
                               padding=1, bias=False, groups=num_group)
        self.bn2 = nn.BatchNorm2d(planes*2)
        self.conv3 = nn.Conv2d(planes*2, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        if planes == 64:
            self.globalAvgPool = nn.AvgPool2d(56, stride=1)
        elif planes == 128:
            self.globalAvgPool = nn.AvgPool2d(28, stride=1)
        elif planes == 256:
            self.globalAvgPool = nn.AvgPool2d(14, stride=1)
        elif planes == 512:
            self.globalAvgPool = nn.AvgPool2d(7, stride=1)
        self.fc1 = nn.Linear(in_features=planes * 4, out_features=round(planes / 4))
        self.fc2 = nn.Linear(in_features=round(planes / 4), out_features=planes * 4)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        original_out = out
        out = self.globalAvgPool(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.sigmoid(out)
        out = out.view(out.size(0), out.size(1), 1, 1)
        out = out * original_out

        out += residual
        out = self.relu(out)

        return out


class SE_ResNeXt(nn.Module):
    def __init__(self, block, layers, num_classes=1000, num_group=32):
        self.inplanes = 64
        super(SE_ResNeXt, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], num_group)
        self.layer2 = self._make_layer(block, 128, layers[1], num_group, stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], num_group, stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], num_group, stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, num_group, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, num_group=num_group))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, num_group=num_group))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def pth2onnx(input_file, output_file):
    model = SE_ResNeXt(Bottleneck, [3, 4, 23, 3])
    model.load_state_dict(torch.load(input_file))
    model.eval()
    input_names = ["image"]
    output_names = ["class"]
    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
    dummy_input = torch.randn(1, 3, 224, 224)
    torch.onnx.export(model, dummy_input, output_file, input_names=input_names,
                      dynamic_axes=dynamic_axes, output_names=output_names, 
                      opset_version=11, verbose=False)


if __name__ == "__main__":
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    pth2onnx(input_file, output_file)